PCA
Kate Weaver
1/3/2022
Setup
library(limma)
library(SummarizedExperiment)
library(rtracklayer)
library(tidyverse)
library(data.table)
library(readxl)
library(janitor)
library(ggrepel)
library(ggthemes)
library(edgeR)
library(plotly)
library(survcomp)
library(cowplot)
library(AnnotationDbi)
library(EnsDb.Hsapiens.v86)
library(ggplot2)importing SummarizedExperiment Data and transforming to raw counts data
load("~/mccoyLab/collabs/doubleseq_2021/summarized_experiment/create_summarized_experiment_allNov2021.Rdata")
counts <- as.matrix(assays(seAll)$counts)
for (col in 1:ncol(counts)){
colnames(counts)[col]<-strsplit(sub("Aligned.sortedByCoord.out.bam", "", colnames(counts)[col]), "_")[[1]][1]
}Subset to just training data/samples
embryoID_by_set <- read.csv("~/mccoyLab/collabs/doubleseq_2021/data_split/embryo_bySet_full_kw_20211217.csv", col.names=c("embryoID", "set"))
train_embryoIDs = embryoID_by_set$embryoID[embryoID_by_set$set == "train"]
train_cols <- which(colnames(counts) %in% train_embryoIDs)
counts <- counts[,train_cols]Subset to genes known in gencode on chr1-22
#subset to genes with matching gencode ensembl gene IDs on chr1-22
file_gencode <- "~/genomes/hg38_genome/gencode.v34.annotation.gtf"
gtf <- rtracklayer::import(file_gencode) %>%
as.data.frame() %>%
dplyr::filter(type == "gene") %>%
dplyr::select(gene_id, seqnames, width) %>%
dplyr::rename(ensembl_gene_id = gene_id) %>%
dplyr::rename(chromosome_name = seqnames) %>%
dplyr::rename(length = width)
gene_table <- gtf[match(rownames(counts), gtf$ensembl_gene_id),]
gene_table <- gene_table[gene_table$chromosome_name %in% paste0("chr", 1:22),]
counts <- counts[gene_table$ensembl_gene_id,]import metadata
meta <- read.csv("~/mccoyLab/collabs/doubleseq_2021/tidied_meta/tidied_meta_CREATE_kw_20220308.csv", row.names = 1) %>%
as.data.frame() %>%
mutate(across(c("AOD", "GC", "Infertility_type", "Previous_pregnancy", "Past_surgical_hist", "Pregnant", "Ongoing_pregnancy", "Final_outcome", "Embryo_grade_at_freezing", "Interpretation", "cDNA_RT_Date", "Library_Prep_Date", "Sequencing_Date", "Study_Participant_ID"), as.factor)) %>%
mutate(across(c("InfD_SSM_GC", "InfD_Egg_factor", "InfD_MF", "InfD_Uterine_factor", "InfD_TF", "InfD_RPL", "InfD_RIF", "InfD_Unexplained", "PMdH_none", "PMdH_vasculitis", "PMdH_immune", "PMdH_stress_hormones"), as.factor))
#subsetting meta data to just the training rows
remove <- setdiff(rownames(meta), colnames(counts))
if (length(which(rownames(meta) %in% remove)) > 0){
meta <- meta[-which(rownames(meta) %in% remove),]
}
#need to sort so name order is the same for setting up design for DESeq experiment
counts_order <- order(colnames(counts))
meta_order <- order(rownames(meta))
countdata <- counts[, counts_order]
coldata <- meta[meta_order, ]#TPM Calculation
calc_tpm <- function(x, gene.length) {
x <- as.matrix(x)
len.norm.lib.size <- colSums(x / gene.length)
return((t(t(x) / len.norm.lib.size) * 1e06)/ gene.length)
}Filter based on expression, non-stringently
tokeep <- rowSums(countdata) > 1
countdatans <- countdata[tokeep, ]
ns_genelengths <- gene_table$length[tokeep]
dgeFullDatans <- DGEList(countdatans, group=as.factor(coldata$Study_Participant_ID))
TMMFullDatans <- calcNormFactors(dgeFullDatans, method="TMM")
rawTPMvalsns <- calc_tpm(TMMFullDatans, gene.length = ns_genelengths)PCA original
pca_resns <- prcomp(t(rawTPMvalsns), scale. = TRUE)
var_explained <- pca_resns$sdev^2/sum(pca_resns$sdev^2)
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Sequencing_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$cDNA_RT_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Library_Prep_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Pregnant)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.integer(coldata$Study_Participant_ID))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$lining_thickness_mm)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Oocyte_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Uterus_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Sperm_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Embryo_grade_at_freezing)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, label=rownames(coldata))) +
geom_point() +
geom_text_repel() +
theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(panel.background = element_blank(), panel.grid = element_blank())
oi1 <- "163-A-1-L"
oi1i <- which(rownames(coldata) == oi1)
oi2 <- "205-A-5-L"
oi2i <- which(rownames(coldata) == oi2)
coloi <- rep(0, length(rownames(coldata)))
coloi[c(oi1i, oi2i)] <- 1
pca_resns$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color = as.factor(coloi), label=rownames(coldata))) +
geom_point() +
geom_text_repel() +
theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(panel.background = element_blank(), panel.grid = element_blank())
Setup cont, filter stringent
Filter based on expression, stringently
#Use more stringent filtering
dgeFullData <- DGEList(countdata, group=as.factor(coldata$Study_Participant_ID))
#normalize counts by TMM
TMMFullData <- calcNormFactors(dgeFullData, method="TMM")
TMMCounts <- as.matrix(TMMFullData$counts)
countsCleaned <- TMMCounts[rowSums(TMMCounts >= 6) > (ncol(TMMCounts)* .2),]
#sum(rownames(dgeFullData$counts) == gene_table$ensembl_gene_id) == length(rownames(dgeFullData$counts)) --> TRUE
#creates a matrix with calculated TPM values for each sample from TMM normalized counts and the gene lengths
rawTPMvals <- calc_tpm(TMMFullData, gene.length = gene_table$length)
cleanedTPMVals <- rawTPMvals[rowSums(rawTPMvals > 0.1) > (ncol(rawTPMvals)*.2),]
cleanCountsDf <- as.data.frame(countsCleaned)
cleanTPMdf <- as.data.frame(cleanedTPMVals)
countdata <- cbind(countdata[intersect(rownames(cleanCountsDf), rownames(cleanTPMdf)),])
final_cleanedTPM <- cbind(rawTPMvals[intersect(rownames(cleanCountsDf), rownames(cleanTPMdf)),])PCA more stringent
pca_res <- prcomp(t(final_cleanedTPM))
var_explained <- pca_res$sdev^2/sum(pca_res$sdev^2)
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Sequencing_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Library_Prep_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$cDNA_RT_Date)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Pregnant)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.integer(coldata$Study_Participant_ID))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$lining_thickness_mm)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Oocyte_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Uterus_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Sperm_Age)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Embryo_grade_at_freezing)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, label=rownames(coldata))) +
geom_point() +
geom_text_repel() +
theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(panel.background = element_blank(), panel.grid = element_blank())
sample_vec <- c("101-A-10-L", "103-A-4-L","104-A-8-L", "107-C-3-L","108-A-3-L", "11-B-12-L", "122-A-4-L", "125-A-4-L", "129-A-3-L", "130-A-13-L", "15-C-4-L", "152-A-5-L", "156-A-2-L", "167-C-4-L", "173-B-5-L",
"179-A-6-L", "180-A-1-L", "185-A-2-L", "2-A-3-L", "202-A-11-L","218-A-5-L",
"31-A-1-L","36-A-2-L", "37-A-4-L","4-B-1-L",
"54-A-10-L","59-B-6-L", "73-A-2-L","75-A-4-L", "88-A-7-L","89-A-2-L", "99-A-3-L" )oii <- which(rownames(coldata) %in% sample_vec)
coloi <- rep(0, length(rownames(coldata)))
coloi[oii] <- 1
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color = as.factor(coloi), label=rownames(coldata))) +
geom_point() +
geom_text_repel() +
theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(panel.background = element_blank(), panel.grid = element_blank())## Warning: ggrepel: 80 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
oi1 <- "163-A-1-L"
oi1i <- which(rownames(coldata) == oi1)
oi2 <- "205-A-5-L"
oi2i <- which(rownames(coldata) == oi2)
coloi <- rep(0, length(rownames(coldata)))
coloi[c(oi1i, oi2i)] <- 1
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color = as.factor(coloi), label=rownames(coldata))) +
geom_point() +
geom_text_repel() +
theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$AOD)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$GC)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$BMI)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Infertility_type)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Previous_pregnancy)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Past_surgical_hist)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$FSH_D3)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$AMH)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$AFC)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$LH)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$E2_Day_2_3)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Type_of_trigger))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Total_Gn)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$E2_on_trigger)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Num_eggs)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Num_MII)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Time_to_stripping_hr)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Time_to_ICSI_hr)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Time_to_Biopsy_hr)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=coldata$Interpretation)) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Natural_cycle))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Medication))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Prednisone))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Fragmin))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$EG))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$HCG))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$IL))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$GCSF))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Metformin))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$PRP))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Sildenafil))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Transferring_physician))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Transfer_catheter_used))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_SSM_GC))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_Egg_factor))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_MF))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_Uterine_factor))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_TF))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_RPL))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_RIF))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$InfD_Unexplained))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$PMdH_none))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$PMdH_vasculitis))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$PMdH_immune))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$PMdH_stress_hormones))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Ongoing_pregnancy))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())
pca_res$x %>%
as.data.frame %>%
ggplot(aes(y=PC1, x=PC2, color=as.factor(coldata$Final_outcome))) +
geom_point() + theme_bw() +
labs(y=paste0("PC1: ", round(var_explained[1]*100,1), "%"),
x=paste0("PC2: ", round(var_explained[2]*100,1), "%")) +
theme(legend.position = "top", panel.background = element_blank(), panel.grid = element_blank())